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Arbitrated Ensemble for Time Series Forecasting

  • Vítor CerqueiraEmail author
  • Luís Torgo
  • Fábio Pinto
  • Carlos Soares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)

Abstract

This paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters.

Keywords

Dynamic ensembles Metalearning Time series Numerical prediction Reproducible research 

Notes

Acknowledgements

This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013; Project “NORTE-01-0145-FEDER-000036” is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work was partly funded by the ECSEL Joint Undertaking, program for research and innovation horizon 2020 (20142020) under grant agreement number 662189-MANTIS-2014-1.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vítor Cerqueira
    • 1
    • 2
    Email author
  • Luís Torgo
    • 1
    • 2
  • Fábio Pinto
    • 1
    • 2
  • Carlos Soares
    • 1
    • 2
  1. 1.University of PortoPortoPortugal
  2. 2.INESC TECPortoPortugal

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